Abstract
The human speech contains and reflects information about the emotional state of the speaker. The importance of research of emotions is increasing in telematics, information technologies and even in health services. The research of the mean acoustical parameters of the emotions is a very complicated task. The emotions are mainly characterized by suprasegmental parameters, but other segmental factors can contribute to the perception of the emotions as well. These parameters are varying within one language, according to speakers etc. In the first part of our research work, human emotion perception was examined. Steps of creating an emotional speech database are presented. The database contains recordings of 3 Hungarian sentences with 8 basic emotions pronounced by nonprofessional speakers. Comparison of perception test results obtained with database recorded by nonprofessional speakers showed similar recognition results as an earlier perception test obtained with professional actors/actresses. It was also made clear, that a neutral sentence before listening to the expression of the emotion pronounced by the same speakers cannot help the perception of the emotion in a great extent. In the second part of our research work, an automatic emotion recognition system was developed. Statistical methods (HMM) were used to train different emotional models. The optimization of the recognition was done by changing the acoustic preprocessing parameters and the number of states of the Markov models.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Amir, N., Ziv, S., Cohen, R.: Characteristics of authentic anger in Hebrew speech. In: Proceedings of Eurospeech 2003, Geneva, pp. 713–716 (2003)
Ang, J., Dhillon, R., Krupski, A., Shipberg, E., Stockle, A.: Prosody-based automatic detection of annoyance and frustration in human-computer dialog. In: Proceedings of ICLSP 2002, Denver, Colorado, pp. 2037–2040 (2002)
Banse, R., Scherer, K.: Acoustic profiles in vocal emotion expression. J. Pers. Social Psychol. 70(3), 614–636 (1996)
Bänziger, T., Scherer, K.R.: Using Actor Portrayals to Systematically Study Multimodal Emotion Expression: The GEMEP Corpus. In: Affective Computing and Intelligent Interaction, pp. 476–487. Springer, Berlin (2007)
Batliner, A., Fischer, K., Huber, R., Spilker, J., Noeth, E.: How to find trouble in communication. Speech Commun. 40, 117–143 (2003)
Douglas-Cowie, E., Campbell, N., Cowie, R., Roach, P.: Emotional speech: towards a new generation of databases. Speech Communication 40, 33–60 (2003)
Engberg, I.S., Hansen, A.V., Andersen, O., Dalsgaard, P.: Design, recording and verification of a Danish Emotional Speech Database. In: Proceedings of the Eurospeech 1997, Rhodes, Greece (1997)
Fék, M., Olaszy, G., Szabó, J., Németh, G., Gordos, G.: Érzelem kifejezése gépi beszéddel. Beszédkutatás 2005. MTA-NyTI, pp. 134–144 (2005)
France, D., Shiavi, R., Silverman, S., Silverman, M., Wilkes, D.: Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans. Biomed. Engng. 47(7), 829–837 (2000)
Groningen corpus S0020 ELRA, http://www.icp.inpg.fr/ELRA
Hozjan, V., Kacic, Z.: A rule-based emotion-dependent feature extraction method for emotion analysis from speech. The Journal of the Acoustical Society of America 119(5), 3109–3120 (2006)
Hozjan, V., Kacic, Z.: Context-Independent Multilingual Emotion Recognition from Speech Signals. International Journal of Speech Technology 6, 311–320 (2003)
HTK: HMM Toolkit; Speech Recognition Toolkit, http://htk.eng.cam.ac.uk/docs/docs
Kienast, M., Sendlmeier, W.F.: Acoustical analysis of spectral and temporal changes in emotional speech. In: Proceedings of the ISCA ITRW on Speech and Emotion, Newcastle, September 5–7, pp. 92–97 (2000)
Mozziconacci, S.: Speech Variability and Emotion: Production and Perception. Technical University of Eindhoven, Proefschrift (1998)
MPEG-4: ISO/IEC 14496 standard (1999), http://www.iec.ch
Navas, E., Hernáez, I., Luengo, I.: An Objective and Subjective Study of the Role of Semantics and Prosodic Features in Building Corpora for Emotional TTS. IEEE transactions on audio, speech, and language processing 14(4) (July 2006)
Sztahó, D.: Emotion perception and automatic emotion recognition in Hungarian. In: Proceedings of Scientific Conference for Students, BME (2007), http://www.vik.bme.hu/tdk
Vicsi, K., Szaszák, G.: Automatic Segmentation for Continuous Speech on Word Level Based Suprasegmental Features. International Journal of Speech Technology (April 2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tóth, S.L., Sztahó, D., Vicsi, K. (2008). Speech Emotion Perception by Human and Machine. In: Esposito, A., Bourbakis, N.G., Avouris, N., Hatzilygeroudis, I. (eds) Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction. Lecture Notes in Computer Science(), vol 5042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70872-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-70872-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70871-1
Online ISBN: 978-3-540-70872-8
eBook Packages: Computer ScienceComputer Science (R0)